Context-aware perception and manipulation strategies for PAPRAS dual-arm stand system in real-world scenarios
Shin, Kazuki
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Permalink
https://hdl.handle.net/2142/120461
Description
Title
Context-aware perception and manipulation strategies for PAPRAS dual-arm stand system in real-world scenarios
Author(s)
Shin, Kazuki
Issue Date
2023-05-04
Director of Research (if dissertation) or Advisor (if thesis)
Kim, Joohyung
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Dual-Arm Robotics
Autonomous Door-Opening
Context-Specific Manipulation
Motion Retargeting
Abstract
Dual-arm robotic systems hold immense potential for performing complex bimanual operations in human-centric environments. However, to fully leverage their capabilities, advanced perception and manipulation strategies tailored to address system constraints and dynamic conditions are crucial. This thesis presents the development and evaluation of context-specific perception and manipulation strategies for the Plug-And-Play-Robotic-Arm-System (PAPRAS) dual-arm stand system. The research is centered on enhancing the adaptability and effectiveness of the system in performing real-world interaction tasks, while considering the specific hardware constraints and requirements of the platform.
To demonstrate the efficacy of the proposed approach, two key applications are explored: autonomous door opening and human-to-robot motion retargeting. These applications exemplify the need for robust perception, coordinated manipulation, and context-aware decision-making. The study integrates camera-based object recognition and localization, task-specific motion planning, and force feedback to address the challenges associated with each application.
The research outcomes highlight the successful implementation of the proposed strategies, leading to improved overall performance and versatility of the PAPRAS dual-arm stand system in diverse and dynamic environments. By integrating unified perception and manipulation strategies with context-specific heuristics, this work contributes to enhancing the capabilities of dual-arm robotic systems for effective and safe human-robot interaction in various applications.
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